Colored interference indentification

ABSTRACT

A method and apparatus for receiving a communication signal r(t) subject to noise n(t) over a communication channel ( 10 ) is disclosed. The method comprises the steps of: receiving ( 100 ) the communication signal r(t) comprising the noise n(t), estimating ( 103 ) the amount of noise n(t) in the communication signal r(t), and determining ( 105 ) the hue of the estimated amount noise n(t), wherein the received communication signal r(t) is passed through a whitening filter if the hue of the noise n(t) is greater than a predetermined threshold level.

TECHNICAL FIELD

Generally, the present invention relates to communication methods andapparatus, and more specifically to a method and apparatus fordetermining the hue of a noise component that is introduced in acommunications signal when it is transferred through a wirelesscommunications system.

DESCRIPTION OF THE PRIOR ART

Many different wireless communications systems have been employed in therecent years to provide voice and data communications to subscribers. Aknown standard for digital cellular mobile telephony is GSM (GlobalSystem for Mobile Service), which today covers a large part of theworld. In the following GSM will be used as a basis for examples anddiscussion, but the description below may in all essential parts beapplied also to other standards for mobile telephony, such as forinstance D-AMPS (Digital Advanced Mobile Phone System) or PDC (PacificDigital Cellular).

The performance of any communications system available today will beaffected by the inevitable noise that arises from various sources. As iswell known, thermal noise will be present in any electrical system dueto e.g. the stochastic motion of electrons acting as charge carriers inthe system. The result from the thermal activity of the electrons isgenerally known as “white noise”.

The term “white” arises from the fact that the noise power is evenlydistributed over the frequency spectrum, i.e. over a long period oftime, the same amount of noise power is present in the range 0-10 kHz asin the range 100 kHz-110 kHz.

In addition to white noise, colored noise is also present in mostelectric systems today. The colored noise may arise from many differentsources such as the metal-oxide-semiconductor junction in a field effecttransistor (FET) which produces what is called pink noise or flickernoise. As will be described below, co-channel and adjacent channelinterference in a communications system will also act as sources ofcolored noise.

The colored noise is characterized by an uneven distribution of noisepower throughout the frequency spectrum. For example, pink noise has thesame distribution of noise power for each octave, so the noise powerbetween 10 kHz and 20 kHz is the same as between 100 kHz and 200 kHz.Many other forms of colored noise are defined and described throughoutthe literature, but they all exhibit the fundamental feature of anuneven noise power distribution.

In wireless communications systems, the background noise in thecommunications channel is normally white. However, the performance ofthese systems are not limited by the background noise only, but more byinterference that arise from other users in the system. As is well knownin the art, the multiple access scheme defines how differentsimultaneous communications between different mobile stations that arelocated in different cells, share the same radio spectrum. In case ofGSM, a multiple access scheme in form of a mix of FDMA (FrequencyDivision Multiple Access) and TDMA (Time Division Multiple Access), hasbeen adopted.

More specifically, in GSM, 124 carrier frequencies with a bandwidth of200 kHz each forms a 25 MHz frequency band using a FDMA scheme. Each ofthe 124 carrier frequencies are then divided in time using a TDMAscheme. This scheme splits the radio channel, with a 200 kHz bandwidth,into eight bursts. A single user is then assigned one burst forcommunication.

For narrow band TDMA systems such as GSM, two types of interference arenormally present. Users with the same carrier create co-channelinterference, while users with adjacent carrier create adjacent channelinterference. As mentioned above, noise due to co-channel and adjacentchannel interference appears in a colored spectrum with a non-uniformdistribution of noise power. Moreover, a receiver filter that isnormally present at the input of the receiver, which is narrower thanthe Nyquist bandwidth can also make the background noise appear colored.

The colored noise significantly impairs the performance of a MostLikelihood Sequence Estimate (MLSE) equalizer present in the receiver,which is only optimal assuming that the present noise is White GausianAdditive Noise (WGAN).

To combat performance degradation due to colored noise, a “whiteningfilter” can be introduced before the equalizer. In addition, unbiasedchannel estimation (“BLUE, best linear unbiased estimation”)may also benecessary. Both whitening filter setting and unbiased channel estimationrequires the knowledge of the noise characteristics which may beobtained by an initial estimation of the noise autocorrelation and theknowledge of the signal information (e.g. through the training sequencein GSM systems).

As mentioned above, whitening filter and unbiased channel estimationimprove the performance of the equalizer considerably when the noise isstrongly colored (i.e. the hue of the noise is high), such as when astrong adjacent channel interference exists. However, when the noise isclose to white, the whitening filter and unbiased channel estimationwill cause performance degradation, which can be significant undercertain circumstances, such as in hilly terrain environment. This isbecause with a limited training sequence length, the estimation of thenoise character can be deficient. At certain level of noise hue, thebenefit of whitening will be outweighed by the impairment due to thedeficiency of in the noise estimation. Further, the use of whiteningfilters and unbiased channel estimation will increase the computationalburden put on the signal processing units in the system.

WO 0139448 A1 discloses a system for whitening a signal disturbance in acommunication signal by using a filter which coefficients are adaptivelyestablished using known signal information in each burst of the receivedsignal. In one embodiment disclosed in WO 0139448 A1, the receivedsignal is processed through a whitening filter having M+1 taps, where Mis a selected integer. The coefficients of the whitening filter arebased on an M-th order linear predictor of the signal disturbance.Alternatively, the coefficients may be based on the autocorrelation ofsignal disturbance. The procedure for performing the whitening of thesignal make heavy demands on the processor performing the calculationeven if none or very little colored noise is present in the signal,since the whitening procedure is performed independent of the hue of thepresent noise.

U.S. Pat. No. 5,031,195 discloses an adaptive modem receiver comprisingan adaptive whitened-matched filter (WMF). The WMF comprises an adaptivelinear equalizer and an adaptive linear predictor. The coefficients ofthe predictor are updated so that the noise at the input of a subsequentsequence decoder is whitened regardless of whether the added noise frompassage through the communication channel is correlated or not. No meansare provided for reducing the computational burden even if the noise iswhite or very little colored.

U.S. Pat. No. 5,283,811 discloses a decision feedback equalizer thatenhances the performance of a receiver when the transmitted signal issubject to multipath propagation which causes delay spread andinter-symbol interference--as a consequence of this. In areas where thedelay spread due to multipath propagation is low (i.e. less than onethird of the symbol duration), the equalizer may be switched out of thecircuit. However, no provisions are disclosed in U.S. Pat. No. 5,283,811how to improve the performance of the receiver based on the colorationof the present noise due to e.g. adjacent channel interference.

SUMMARY OF THE INVENTION

The present invention seeks to provide a method for improving theperformance of a communication apparatus receiving a signal that issubject to noise arising from e.g. adjacent channel interference andco-channel interference.

This object has been achieved by a method for receiving a communicationsignal r(t) subject to noise n(t) over a communication channelcomprising the steps of: 1) receiving (100) the communication signalr(t) comprising the noise n(t), 2) estimating (103) the noise power n(t)in the communication signal r(t), 3) determining (105) the hue of theestimated noise n(t), wherein the received communication signal r(t), ispassed through a whitening filter if the hue of the noise n(t) isgreater than a predetermined threshold level.

According to a preferred embodiment, the method is performed by acommunication apparatus comprising: receiving circuitry (30, 40, 50) forreceiving a communication signal r(t) subject to noise n(t) over acommunication channel and a signal processing unit (60) adapted to: 1)estimating (103) the noise power n(t) in the communication signal r(t),2) determining (105) the hue of the estimated noise n(t), and engaging(107) a whitening filter (80) if the hue of the noise n(t) is greaterthan a predetermined threshold level.

Other objects, features and advantages of the present invention willappear more clearly from the following detailed disclosure of apreferred embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

A preferred embodiment of the present invention will now be describedwith reference to the accompanying drawings, in which:

FIG. 1 is a schematic block diagram illustrating the differentprocessing blocks according to a preferred embodiment,

FIG. 2 a is a schematic graph illustrating the result from calculatingthe autocorrelation of an information sequence r(m),

FIG. 2 b is a schematic graph illustrating the result from calculatingthe autocorrelation of white noise,

FIG. 2 c is a schematic graph illustrating the result from calculatingthe autocorrelation of colored noise, and

FIG. 3 is a schematic flow chart illustrating the steps for determiningthe hue of a noise disturbance in a signal according to a preferredembodiment.

DETAILED DISCLOSURE OF A PREFERRED EMBODIMENT

FIG. 1 gives an overview of a receiver in a communications systemaccording to a preferred embodiment of the present-invention. Theinformation signal s(t) is transmitted over a communications channel 10in the form of radio waves. The information-carrying medium is, however,of less importance for the function of the invention and the informationmay as well be transmitted by means of light, cable or any othersuitable communications medium. For simplicity reasons, however, onlycommunication by means of radio waves will be disclosed throughout thistext.

Before being dispatched from the transmitter 20 in FIG. 1, the signals(t) modulates a high frequency down link carrier, which in the case ofGSM communication is in the range of 935-960 MHz. The output of thetransmitter is hence a HF-signal (i.e. carrier envelope modulated bys(t)) that is suitable for transmission.

Irrespective of which communications medium that is selected fortransmission of the information signal s(t), the signal s(t) will bealtered in that disturbances n(t) associated with channel 10characteristics will be introduced during transmission through theactual communications channel 10. The disturbances arise, as mentionedabove, from many different sources, of which co-channel and adjacentchannel interference is of major concern.

The HF-signal is received in the HF circuitry 30, which in a preferredembodiment operates according to the homodyne principle, and thereceived information signal r(t) is accordingly extracted from thereceived HF-signal by mixing the received HF-signal with a signal from alocal oscillator 31. As is the case with signal modulation mentionedabove, signal demodulation according to the homodyne or heterodyneprinciple is well known in the art and is readily found in theliterature. However, any other suitable demodulation principle ispossible within the scope of the invention.

After removal of the high frequency carrier, the received base bandsignal r(t) is transferred to an analog to digital converter (ADC) 40for converting the analog signal r(t) into a time-discreet digitalsignal r(m). The sampled and converted signal r(m) is then, afterfiltering in a low-pass filter 50, received in a signal processing unit60 which in a preferred embodiment is in form of a DSP (digital signalprocessor) that performs the steps disclosed below by executing programcode being stored in a memory 61. The signal processing unit may howeveras well be realized in form e.g. a FPGA (Field Programmable Gate Array)or an ASIC (Application Specific Integrated Circuit).

The signal processing unit performs a first initial channel estimationafter burst synchronization based on known signal information (i.e. thetraining sequence in case of GSM/EDGE) found in the received signalr(m). The received signal r(m)is compared to the expected symbolsequence in order to determine the noise samples n(m) according to theformula: $\begin{matrix}{n_{k} = {r_{k} - {\sum\limits_{\underset{\_}{i} = 0}^{{Ms} - 1}{h_{i}s_{k - 1}}}}} & (1)\end{matrix}$

The embedded training sequences are normally short in length. Thisimplies that it is very difficult to determine the noise characteristicsby analyzing the sequence in the time domain. In this case, thecalculation of the autocorrelation of the noise n(t) is a powerful toolfor obtaining information regarding the noise spectrum.

Generally, the estimation of the noise autocorrelation (assumezero-mean) is calculated from the estimated noise samples according tothe formula: $\begin{matrix}{\rho_{k} = {\frac{1}{N}{\sum\limits_{i = 0}^{{Ns} - k - 1}{n_{i}^{*}n_{i + k}}}}} & (2)\end{matrix}$where Ns is the number of estimated noise samples and where ( )* denotescomplex conjugate. Noise autocorrelation is in general a complex,conjugate-symmetrical sequence so that the negative indexed elements canbe obtained from the positive indexed part, ρ_(−k)=ρ*. From the aboveequation, ρ₀ is always a real element.

FIG. 2 a illustrates the result from calculating the autocorrelation ofan information sequence r(m). As is seen from FIG. 2 a, the result ofthe autocorrelation is a vector, in this case presented as a graph, withits center at the Y-axis (i.e. zero lag), and which then decays towardszero as the lag increases (or the lead increases in case future signalvalues are known). Consequently, the information sequence shows a highdegree of autocorrelation between adjacent and near-adjacent samples.

In case there were no information signal present, i.e. the signalcomprises noise n(m) only, as seen in FIG. 2 b, the autocorrelation isalmost zero for every lag (except for lag=0, which is always 1 bydefinition) and without any dominant peaks as found in FIG. 2 a

FIG. 2 c illustrates an autocorrelation of the noise n(m) in the casewhere it is not white and uniformly distributed over the spectrum butrather colored in the sense that the noise power is higher at some partsof the spectrum. This happens, e.g. in channels with strong adjacentchannel interference. As can be seen from FIG. 2 c, the colored noiseshows a degree of autocorrelation since the output from theautocorrelation calculation is non-zero and decaying for lags other thanzero. A stronger coloration of the noise (i.e. a higher hue value) willresult in an output from the autocorrelation calculation that has highervalues for lags other than zero than a weak colored signal disturbance.

The signal processing unit 60 then determines the center of gravity ofthe autocorrelation function according to the formula: $\begin{matrix}{\sigma = \frac{\sum\limits_{k = 0}^{{Ns} - 1}{\left( {k + 1} \right){\rho_{k}}^{2}}}{\sum\limits_{k = 0}^{{Ns} - 1}{\rho_{k}}^{2}}} & (3)\end{matrix}$

Note that in a preferred embodiment, the formula uses the term k+1instead of k in the numerator to preserve the weight of the first andmost important element in the noise autocorrelation. However, otherfunctions for determining the center of gravity for a function may aswell be used.

The stronger colored the noise is, the higher value for the result ofthe calculation of the center of gravity σ will be obtained. This is aconsequence of the fact that a noise signal with a strong colorationwill result in an autocorrelation with high values for lags other thanzero and that the center of gravity is calculated for one side of theautocorrelation only, i.e. only autocorrelation values to the right ofthe Y-axis are taken into consideration.

The hue of the noise can thus be determined by a single variable σ. Athreshold can then be set according to N and s to switch on/off thewhitening filter/unbiased estimation. In practice, for example, it isfound that for GSM/EDGE, with a training sequence of 26 symbols in anormal burst, a threshold can be experimentally set as:σ_(T)=1+s

Note that the threshold is not proportional to the oversampling rate inthis case.

If σ is less than σ_(T), the noise is considered white and the whiteningfilter and the unbiased estimation (BLUE) are bypassed by the coloredinterference identification block 100. Instead a much simpler leastsquare estimation 70 can be used to estimate the channel impulseresponse. This will reduce the need for computational power in thesignal processing unit 60 which in turn makes it possible to reduce thesystem clock frequency of the signal processing unit 60 by use of e.g.PLL-techniques. As is well known, reducing the clock frequency of anelectronic system will also reduce the power consumption of the system.For a given battery capacity, the system will hence be operational for alonger time.

On the other hand, if the threshold σ is greater than σ_(T), the noiseis not white wherein the unbiased channel estimation and the whiteningfilter 80 are introduced before the equalizer 90.

FIG. 3 illustrates a flowchart for determining the hue of the noisen(m). The procedure starts in step 100 with the reception of the baseband signal corresponding to the training sequence. As mentioned above,it is understood in this context that the information signal r(t)normally is obtained by demodulating a HF-signal. Regardless of the whattransmission procedures that may have preceded the reception (i.e. anyHF modulation-demodulation procedures normally performed whentransmitting a signal), the received base band signal comprises both theexpected training sequence s(t) through the propagation channel as wellas noise n(t).

Before further processing, the signal is converted into a time-discreetdigital signal r(m) by an Analog to Digital converter (ADC) in step 101.The ADC is normally of an over-sampling type (i.e. the signal is sampledmore than twice the highest frequency component in the signal) but mayas well be of a Nyquist-type (i.e. the signal is sampled twice thehighest frequency component in the signal).

The A/D-converted signal r(m) is low pass-filtered before transferred tothe Signal processing unit 60 where a first estimation of the noisepower n(m) is performed in step 103. As described above, this ispossible due to a known signal sequence (i.e. the training sequence inGSM) found in the received signal r(m). Since the training sequence willbe distorted by the channel characteristics (i.e. noise is added, e.g.multi path and additive interference), it is possible to determine thenoise in the signal from comparing the received signal r(m) with theknown training sequence.

An autocorrelation of the estimated noise signal n(m) is calculated instep 104. The autocorrelation will reveal the frequency characteristicsof the noise signal even if the signal is very short in length. Mostdigital signal processors (DSP) available today are adapted to performautocorrelation calculations in an efficient way which implies that theautocorrelation calculation is not a major burden from a processingpoint of view.

In step 105 the center of gravity a of the autocorrelated noise iscalculated according to equation 3. If the hue of the noise n(m) is low,the center of gravity will be located close to lag=0 since the energy atlags<>0 is very low, as is seen in FIG. 2 b. However, if the hue isincreasing due to e.g. adjacent channel interference, the center ofgravity will be pushed further away from the Y-axis as is seen in FIG. 2c.

The signal-processing unit 60 determines in step 106 if the center ofgravity σ is above a preset threshold σ_(T) which may be based onempirical as well as mathematical grounds.

If the center of gravity σ is above the threshold level σ_(T), thesignal-processing unit 60, via the colored interference identificationblock 100, activates the whitening filter 80 and the unbiased channelestimation in step 107. It is appreciated that the whiteningfilter/unbiased channel estimation 80 may be performed by the signalprocessing unit 60 itself, by a separate DSP, by a fixed logic such as aFPGA (Field Programmable Gate Array) or by an ASIC (Application SpecificIntegrated Circuit). The whitening filter will then provide a signalwith less coloration to the equalizer (or demodulator) 90, which in turnwill increase the equalizer performance since the equalizer assumes thatthe disturbances introduced by the channel are white.

If, however, the center of gravity σ is below the threshold σ_(T), thewhitening filter is not activated since the noise is considered white.An application of the whitening filter on a signal comprising whitenoise will not only increase the computational burden as describedabove, but will in most cases also lower the efficiency of the decodingprocedure in the equalizer 90. Instead the whitening filter and theunbiased channel estimation is bypassed.

The present invention has been described above with reference to apreferred embodiment. However, other embodiments than the one disclosedherein are possible within the scope of the invention, as defined by theappended independent claims.

1. A method for receiving a communication signal r(t) subject to a noisen(t) over a communication channel comprising the steps of: receiving(100) the communication signal r(t) comprising the noise n(t),estimating (103) the amount of noise n(t) in the communication signalr(t), and determining (105) the hue of the estimated amount of the noisen(t), comparing (106) the hue of the estimated amount of noise n(t) to apredetermined threshold, and passing the received communication signalr(t) through a whitening filter (80) if the hue of the noise n(t) isgreater than a predetermined threshold level or bypassing said whiteningfilter (80) if the hue of the noise n(t) is lower than saidpredetermined threshold level.
 2. A method according to claim 1, whereinsaid noise n(t) is estimated (103) by means of comparing the receivedsignal r(t) with known signal information.
 3. A method according toclaim 1 or 2, wherein frequency characteristics of the noise n(t) isdetermined (104) by performing an autocorrelation of the noise n(t). 4.A method according to claim 3, wherein the hue of the noise n(t) isdetermined (105) by determining the center of gravity σ of the noiseautocorrelation sequence.
 5. A method according to claim 4, wherein saidnoise hue is determined by a single variable.
 6. A method according toany of claims 4 or 5, wherein the hue of the noise is determinedaccording to the formula:$\sigma = \frac{\sum\limits_{k = 0}^{{Ns} - 1}{\left( {k + 1} \right){\rho }^{2}}}{\sum\limits_{k = 0}^{{Ns} - 1}{\rho }^{2}}$7. A method according to claim 1, wherein the threshold level engages anunbiased channel estimation.
 8. A communication apparatus comprising:receiving circuitry (30, 40, 50) for receiving a communication signalr(t) subject to a noise n(t) over a communication channel; a signalprocessing unit (60) adapted to: estimating (103) the amount of noisen(t) in the communication signal r(t); determining(105) the hue of theestimated amount of noise n(t); comparing (106) the hue of the estimatedamount of noise n(t) to a predetermined threshold; passing the receivedcommunication signal r(t) through a whitening filter (80) if the hue ofthe noise n(t) is greater than a predetermined threshold level orbypassing said whitening filter (80) if the hue of the noise n(t) islower than said predetermined threshold level.
 9. An apparatus accordingto claim 8, wherein the signal processing unit (60) is adapted toestimate (103) the noise n(t) by means of comparing the received signalr(t) with known signal information.
 10. An apparatus according to claim8 or 9, wherein the signal processing unit (60) is adapted to determine(104) the frequency characteristics of the noise n(t) by performing anautocorrelation of the noise n(t).
 11. An apparatus according to claim10, wherein the signal processing unit (60) is adapted to determine(105) the hue of the noise n(t) by determining the center of gravity σof the noise autocorrelation sequence.
 12. An apparatus according toclaim 11, wherein the signal processing unit (60) is adapted todetermine the noise hue by a single variable.
 13. An apparatus accordingto any of claims 11 or 12, wherein signal processing unit (60) isadapted to determine the hue of the noise according to the formula:$\sigma = \frac{\sum\limits_{k = 0}^{{Ns} - 1}{\left( {k + 1} \right){\rho }^{2}}}{\sum\limits_{k = 0}^{{Ns} - 1}{\rho }^{2}}$14. An apparatus according to claim 8, wherein the signal processingunit (60) is adapted to engage an unbiased channel estimation if the hueof the noise n(t) is greater than a predetermined threshold level. 15.An apparatus according to any of claims 8-14, wherein the signalprocessing unit (60) is a digital signal processor (DSP).
 16. A computerprogram product directly loadable into an internal memory (61)associated with a processor (60), said processor being operativelycoupled to receiving circuitry (30, 40, 50) for receiving acommunication signal r(t) subject to a noise n(t) over a communicationchannel, comprising program code for estimating (103) the amount ofnoise n(t) in the communication signal r(t); determining (105) the hueof the estimated amount of noise n(t); comparing (106) the hue of theestimated amount of noise n(t) to a predetermined threshold; passing thereceived communication signal r(t) through a whitening filter (80) ifthe hue of the noise n(t) is greater than a predetermined thresholdlevel or bypassing said whitening filter (80) if the hue of the noisen(t) is lower than said predetermined threshold level when executed bysaid processor.
 17. A computer program product as defined in claim 16,embodied on a computer-readable medium.